Intrusion Detection for Smart Grid Communication Systems

Transformation of the traditional power grid into a smart grid hosts an array of vulnerabilities associated with communication networks. Furthermore, wireless mediums used throughout the smart grid promote an environment where Denial of Service (DoS) attacks are very effective. In wireless mediums, jamming and spoofing attack techniques diminish system operations thus affecting smart grid stability and posing an immediate threat to Confidentiality, Integrity, and Availability (CIA) of the smart grid. Intrusion detection systems (IDS) serve as a primary defense in mitigating network vulnerabilities. In IDS, signatures created from historical data are compared to incoming network traffic to identify abnormalities. In this thesis, intrusion detection algorithms are proposed for attack detection in smart grid networks by means of physical, data link, network, and session layer analysis. Irregularities in these layers provide insight to whether the network is experiencing genuine or malicious activity

Game-Theoretic and Machine-Learning Techniques for Cyber-Physical Security and Resilience in Smart Grid

The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources. This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements. Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Towards Secure Deep Neural Networks for Cyber-Physical Systems

In recent years, deep neural networks (DNNs) are increasingly investigated in the literature to be employed in cyber-physical systems (CPSs). DNNs own inherent advantages in complex pattern identifying and achieve state-of-the-art performances in many important CPS applications. However, DNN-based systems usually require large datasets for model training, which introduces new data management issues. Meanwhile, research in the computer vision domain demonstrated that the DNNs are highly vulnerable to adversarial examples. Therefore, the security risks of employing DNNs in CPSs applications are of concern. In this dissertation, we study the security of employing DNNs in CPSs from both the data domain and learning domain. For the data domain, we study the data privacy issues of outsourcing the CPS data to cloud service providers (CSP). We design a space-efficient searchable symmetric encryption scheme that allows the user to query keywords over the encrypted CPS data that is stored in the cloud. After that, we study the security risks that adversarial machine learning (AML) can bring to the CPSs. Based on the attacker properties, we further separate AML in CPS into the customer domain and control domain. We analyze the DNN-based energy theft detection in advanced meter infrastructure as an example for customer domain attacks. The adversarial attacks to control domain CPS applications are more challenging and stringent. We then propose ConAML, a general AML framework that enables the attacker to generate adversarial examples under practical constraints. We evaluate the framework with three CPS applications in transportation systems, power grids, and water systems. To mitigate the threat of adversarial attacks, more robust DNNs are required for critical CPSs. We summarize the defense requirements for CPS applications and evaluate several typical defense mechanisms. For control domain adversarial attacks, we demonstrate that defensive methods like adversarial detection are not capable due to the practical attack requirements. We propose a random padding framework that can significantly increase the DNN robustness under adversarial attacks. The evaluation results show that our padding framework can reduce the effectiveness of adversarial examples in both customer domain and control domain applications

Intrusion Detection in Industrial Networks via Data Streaming

Given the increasing threat surface of industrial networks due to distributed, Internet-of-Things (IoT) based system architectures, detecting intrusions in\ua0 Industrial IoT (IIoT) systems is all the more important, due to the safety implications of potential threats. The continuously generated data in such systems form both a challenge but also a possibility: data volumes/rates are high and require processing and communication capacity but they contain information useful for system operation and for detection of unwanted situations.In this chapter we explain that\ua0 stream processing (a.k.a. data streaming) is an emerging useful approach both for general applications and for intrusion detection in particular, especially since it can enable data analysis to be carried out in the continuum of edge-fog-cloud distributed architectures of industrial networks, thus reducing communication latency and gradually filtering and aggregating data volumes. We argue that usefulness stems also due to\ua0 facilitating provisioning of agile responses, i.e. due to potentially smaller latency for intrusion detection and hence also improved possibilities for intrusion mitigation. In the chapter we outline architectural features of IIoT networks, potential threats and examples of state-of-the art intrusion detection methodologies. Moreover, we give an overview of how leveraging distributed and parallel execution of streaming applications in industrial setups can influence the possibilities of protecting these systems. In these contexts, we give examples using electricity networks (a.k.a. Smart Grid systems).We conclude that future industrial networks, especially their Intrusion Detection Systems (IDSs), should take advantage of data streaming concept by decoupling semantics from the deployment

A Survey on Energy Efficiency in Smart Homes and Smart Grids

Empowered by the emergence of novel information and communication technologies (ICTs) such as sensors and high-performance digital communication systems, Europe has adapted its electricity distribution network into a modern infrastructure known as a smart grid (SG). The benefits of this new infrastructure include precise and real-time capacity for measuring and monitoring the different energy-relevant parameters on the various points of the grid and for the remote operation and optimization of distribution. Furthermore, a new user profile is derived from this novel infrastructure, known as a prosumer (a user that can produce and consume energy to/from the grid), who can benefit from the features derived from applying advanced analytics and semantic technologies in the rich amount of big data generated by the different subsystems. However, this novel, highly interconnected infrastructure also presents some significant drawbacks, like those related to information security (IS). We provide a systematic literature survey of the ICT-empowered environments that comprise SGs and homes, and the application of modern artificial intelligence (AI) related technologies with sensor fusion systems and actuators, ensuring energy efficiency in such systems. Furthermore, we outline the current challenges and outlook for this field. These address new developments on microgrids, and data-driven energy efficiency that leads to better knowledge representation and decision-making for smart homes and SGsThis research was co-funded by Interreg Österreich-Bayern 2014–2020 programme project KI-Net: Bausteine für KI-basierte Optimierungen in der industriellen Fertigung (AB 292). This work is also supported by the ITEA3 OPTIMUM project and ITEA3 SCRATCH project, all of them funded by the Centro Tecnológico de Desarrollo Industrial (CDTI), Spain

Strategic Optimization Techniques For FRTU Deployment and Chip Physical Design

Combinatorial optimization is a complex engineering subject. Although formulation often depends on the nature of problems that differs from their setup, design, constraints, and implications, establishing a unifying framework is essential. This dissertation investigates the unique features of three important optimization problems that can span from small-scale design automation to large-scale power system planning: (1) Feeder remote terminal unit (FRTU) planning strategy by considering the cybersecurity of secondary distribution network in electrical distribution grid, (2) physical-level synthesis for microfluidic lab-on-a-chip, and (3) discrete gate sizing in very-large-scale integration (VLSI) circuit. First, an optimization technique by cross entropy is proposed to handle FRTU deployment in primary network considering cybersecurity of secondary distribution network. While it is constrained by monetary budget on the number of deployed FRTUs, the proposed algorithm identi?es pivotal locations of a distribution feeder to install the FRTUs in different time horizons. Then, multi-scale optimization techniques are proposed for digital micro?uidic lab-on-a-chip physical level synthesis. The proposed techniques handle the variation-aware lab-on-a-chip placement and routing co-design while satisfying all constraints, and considering contamination and defect. Last, the first fully polynomial time approximation scheme (FPTAS) is proposed for the delay driven discrete gate sizing problem, which explores the theoretical view since the existing works are heuristics with no performance guarantee. The intellectual contribution of the proposed methods establishes a novel paradigm bridging the gaps between professional communities